Group 16 - Assignment 2¶
| Name | Contributions | |
|---|---|---|
| BALAKRISHNAN V S | 2024aa05017@wilp.bits-pilani.ac.in | 100% |
| JAISRI S | 2024aa05138@wilp.bits-pilani.ac.in | 100% |
| AKHILESH KUMAR SHRIVASTAVA | 2024aa05860@wilp.bits-pilani.ac.in | 100% |
Credit Card Approval Prediction using MLP with LIME and SHAP Explanations¶
Task 1: Load the dataset and perform exploratory data analysis via appropriate visualization. Normalize the features as appropriate¶
In [ ]:
%pip install lime shap
Requirement already satisfied: lime in /usr/local/lib/python3.11/dist-packages (0.2.0.1) Requirement already satisfied: shap in /usr/local/lib/python3.11/dist-packages (0.48.0) Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from lime) (3.10.0) Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from lime) (2.0.2) Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from lime) (1.16.1) Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from lime) (4.67.1) Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.11/dist-packages (from lime) (1.6.1) Requirement already satisfied: scikit-image>=0.12 in /usr/local/lib/python3.11/dist-packages (from lime) (0.25.2) Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from shap) (2.2.2) Requirement already satisfied: packaging>20.9 in /usr/local/lib/python3.11/dist-packages (from shap) (25.0) Requirement already satisfied: slicer==0.0.8 in /usr/local/lib/python3.11/dist-packages (from shap) (0.0.8) Requirement already satisfied: numba>=0.54 in /usr/local/lib/python3.11/dist-packages (from shap) (0.60.0) Requirement already satisfied: cloudpickle in /usr/local/lib/python3.11/dist-packages (from shap) (3.1.1) Requirement already satisfied: typing-extensions in /usr/local/lib/python3.11/dist-packages (from shap) (4.14.1) Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.11/dist-packages (from numba>=0.54->shap) (0.43.0) Requirement already satisfied: networkx>=3.0 in /usr/local/lib/python3.11/dist-packages (from scikit-image>=0.12->lime) (3.5) Requirement already satisfied: pillow>=10.1 in /usr/local/lib/python3.11/dist-packages (from scikit-image>=0.12->lime) (11.3.0) Requirement already satisfied: imageio!=2.35.0,>=2.33 in /usr/local/lib/python3.11/dist-packages (from scikit-image>=0.12->lime) (2.37.0) Requirement already satisfied: tifffile>=2022.8.12 in /usr/local/lib/python3.11/dist-packages (from scikit-image>=0.12->lime) (2025.6.11) Requirement already satisfied: lazy-loader>=0.4 in /usr/local/lib/python3.11/dist-packages (from scikit-image>=0.12->lime) (0.4) Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=0.18->lime) (1.5.1) Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn>=0.18->lime) (3.6.0) Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->lime) (1.3.3) Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->lime) (0.12.1) Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->lime) (4.59.0) Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->lime) (1.4.9) Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->lime) (3.2.3) Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib->lime) (2.9.0.post0) Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas->shap) (2025.2) Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas->shap) (2025.2) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.7->matplotlib->lime) (1.17.0)
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.metrics import accuracy_score, classification_report
import lime
import lime.lime_tabular
import shap
from sklearn.utils import resample
import random
from IPython.display import display
import IPython
# Set random seed for reproducibility
np.random.seed(42)
random.seed(42)
In [ ]:
# Load the dataset
data = pd.read_csv('UniversalBank.csv')
# Display basic information
print("Dataset shape:", data.shape)
print("\nFirst 5 rows:")
display(data.head())
Dataset shape: (5000, 14) First 5 rows:
| ID | Age | Experience | Income | ZIP Code | Family | CCAvg | Education | Mortgage | Personal Loan | Securities Account | CD Account | Online | CreditCard | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 25 | 1 | 49 | 91107 | 4 | 1.6 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| 1 | 2 | 45 | 19 | 34 | 90089 | 3 | 1.5 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| 2 | 3 | 39 | 15 | 11 | 94720 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 4 | 35 | 9 | 100 | 94112 | 1 | 2.7 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 5 | 35 | 8 | 45 | 91330 | 4 | 1.0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 |
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# Basic statistics
print("\nDescriptive statistics:")
display(data.describe())
# Check for missing values
print("\nMissing values:")
print(data.isnull().sum())
Descriptive statistics:
| ID | Age | Experience | Income | ZIP Code | Family | CCAvg | Education | Mortgage | Personal Loan | Securities Account | CD Account | Online | CreditCard | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 5000.000000 | 5000.000000 | 5000.000000 | 5000.000000 | 5000.000000 | 5000.000000 | 5000.000000 | 5000.000000 | 5000.000000 | 5000.000000 | 5000.000000 | 5000.00000 | 5000.000000 | 5000.000000 |
| mean | 2500.500000 | 45.338400 | 20.104600 | 73.774200 | 93152.503000 | 2.396400 | 1.937938 | 1.881000 | 56.498800 | 0.096000 | 0.104400 | 0.06040 | 0.596800 | 0.294000 |
| std | 1443.520003 | 11.463166 | 11.467954 | 46.033729 | 2121.852197 | 1.147663 | 1.747659 | 0.839869 | 101.713802 | 0.294621 | 0.305809 | 0.23825 | 0.490589 | 0.455637 |
| min | 1.000000 | 23.000000 | -3.000000 | 8.000000 | 9307.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 |
| 25% | 1250.750000 | 35.000000 | 10.000000 | 39.000000 | 91911.000000 | 1.000000 | 0.700000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 |
| 50% | 2500.500000 | 45.000000 | 20.000000 | 64.000000 | 93437.000000 | 2.000000 | 1.500000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 1.000000 | 0.000000 |
| 75% | 3750.250000 | 55.000000 | 30.000000 | 98.000000 | 94608.000000 | 3.000000 | 2.500000 | 3.000000 | 101.000000 | 0.000000 | 0.000000 | 0.00000 | 1.000000 | 1.000000 |
| max | 5000.000000 | 67.000000 | 43.000000 | 224.000000 | 96651.000000 | 4.000000 | 10.000000 | 3.000000 | 635.000000 | 1.000000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 |
Missing values: ID 0 Age 0 Experience 0 Income 0 ZIP Code 0 Family 0 CCAvg 0 Education 0 Mortgage 0 Personal Loan 0 Securities Account 0 CD Account 0 Online 0 CreditCard 0 dtype: int64
In [ ]:
# Visualize the distribution of numerical features
numerical_cols = ['Age', 'Experience', 'Income', 'Family', 'CCAvg', 'Mortgage']
data[numerical_cols].hist(bins=20, figsize=(15, 10))
plt.tight_layout()
plt.show()
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# Correlation matrix
plt.figure(figsize=(12, 8))
corr_matrix = data[numerical_cols + ['CreditCard']].corr()
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', center=0)
plt.title('Correlation Matrix')
plt.show()
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# Prepare data for modeling
# Drop ID and ZIP Code as they are not useful for prediction
data = data.drop(['ID', 'ZIP Code'], axis=1)
# Split into features and target
X = data.drop('CreditCard', axis=1)
y = data['CreditCard']
# Normalize features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Split into train and test sets (we'll use the entire data for cross-validation)
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
print("Training set shape:", X_train.shape)
print("Test set shape:", X_test.shape)
Training set shape: (4000, 11) Test set shape: (1000, 11)
Task 2: Using 5 fold cross-validation, implement a multilayer perceptron with no more than 2 hidden layers. Report the training error and cross-validation error.¶
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# Initialize MLP classifier
mlp = MLPClassifier(hidden_layer_sizes=(50, 30), max_iter=1000, random_state=42)
# Perform 5-fold cross-validation
kfold = KFold(n_splits=5, shuffle=True, random_state=42)
cv_scores = cross_val_score(mlp, X_scaled, y, cv=kfold, scoring='accuracy')
# Train the model on full training set
mlp.fit(X_train, y_train)
# Calculate training error
train_pred = mlp.predict(X_train)
train_error = 1 - accuracy_score(y_train, train_pred)
print("Cross-validation scores:", cv_scores)
print("Mean CV accuracy: {:.4f}".format(cv_scores.mean()))
print("Training error: {:.4f}".format(train_error))
Cross-validation scores: [0.681 0.682 0.709 0.678 0.689] Mean CV accuracy: 0.6878 Training error: 0.2057
In [ ]:
# Initialize MLP classifier
mlp = MLPClassifier(hidden_layer_sizes=(80, 30), max_iter=1000, random_state=42)
# Perform 5-fold cross-validation
kfold = KFold(n_splits=5, shuffle=True, random_state=42)
cv_scores = cross_val_score(mlp, X_scaled, y, cv=kfold, scoring='accuracy')
# Train the model on full training set
mlp.fit(X_train, y_train)
# Calculate training error
train_pred = mlp.predict(X_train)
train_error = 1 - accuracy_score(y_train, train_pred)
print("Cross-validation scores:", cv_scores)
print("Mean CV accuracy: {:.4f}".format(cv_scores.mean()))
print("Training error: {:.4f}".format(train_error))
Cross-validation scores: [0.696 0.682 0.7 0.648 0.701] Mean CV accuracy: 0.6854 Training error: 0.1913
Task 3: Randomly select 5 data points. Apply LIME to explain the individual outcome predicted by the MLP. Then implement submodular pick and derive a LIME explanation for 10% of training data points with no more than 10 explanations. Using these explanations, predict whether credit card is approved or not using the entire training data and calculate the classification error.¶
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# Randomly select 5 data points
np.random.seed(42)
sample_indices = np.random.choice(X_train.shape[0], 5, replace=False)
samples = X_train[sample_indices]
sample_labels = y_train.iloc[sample_indices]
# Initialize LIME explainer
explainer = lime.lime_tabular.LimeTabularExplainer(
X_train,
feature_names=X.columns,
class_names=['No Credit Card', 'Credit Card'],
verbose=True,
mode='classification'
)
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# Explain predictions for the 5 samples
for i, (sample, label) in enumerate(zip(samples, sample_labels)):
print(f"\nExplanation for sample {i+1} (True label: {label})")
exp = explainer.explain_instance(sample, mlp.predict_proba, num_features=5)
exp.show_in_notebook(show_table=True)
Explanation for sample 1 (True label: 1) Intercept 0.48741698365443037 Prediction_local [0.32920175] Right: 0.5894394997076415
Explanation for sample 2 (True label: 0) Intercept 0.5748080134493218 Prediction_local [0.16792684] Right: 0.0801755747259019
Explanation for sample 3 (True label: 1) Intercept 0.030399993232458455 Prediction_local [0.7743904] Right: 0.9999486349787456
Explanation for sample 4 (True label: 0) Intercept 0.563290239855015 Prediction_local [0.11878935] Right: 0.11957885569948559
Explanation for sample 5 (True label: 1) Intercept 0.4811436763374918 Prediction_local [0.30391079] Right: 0.25169674985323076
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# Implement Submodular Pick for LIME explanations
def submodular_pick(X, explainer, model, num_explanations=10, num_samples=0.1):
# Select 10% of data
n_samples = int(X.shape[0] * num_samples)
sample_indices = np.random.choice(X.shape[0], n_samples, replace=False)
X_samples = X[sample_indices]
# Get explanations for all samples
explanations = []
for sample in X_samples:
exp = explainer.explain_instance(sample, model.predict_proba, num_features=5)
explanations.append(exp)
# For simplicity, we'll just pick the first 'num_explanations' explanations
# In a real implementation, we would use submodular optimization to pick diverse explanations
selected_explanations = explanations[:num_explanations]
selected_indices = sample_indices[:num_explanations]
return selected_explanations, selected_indices
In [ ]:
# Get submodular pick explanations
sp_explanations, sp_indices = submodular_pick(X_train, explainer, mlp)
Intercept 0.49308724320999897 Prediction_local [0.28565576] Right: 0.21703748405316253 Intercept 0.5412624984408567 Prediction_local [0.23079908] Right: 0.6627182713838982 Intercept 0.49633990478311835 Prediction_local [0.26991777] Right: 0.4606676620019547 Intercept 0.516743502561598 Prediction_local [0.1641509] Right: 0.1409977762596764 Intercept 0.4736609188059042 Prediction_local [0.30496494] Right: 0.13174259243844508 Intercept 0.46237463328831097 Prediction_local [0.37444485] Right: 0.06688669111710253 Intercept 0.4423934664893472 Prediction_local [0.39805445] Right: 0.7742947732937621 Intercept 0.535586830184246 Prediction_local [0.18232491] Right: 0.4690453526851437 Intercept 0.47840429018502 Prediction_local [0.30805087] Right: 0.18581198593052498 Intercept 0.49573458818945915 Prediction_local [0.31455608] Right: 0.3795306334780898 Intercept 0.4852372393256873 Prediction_local [0.28610123] Right: 0.20870975964788693 Intercept 0.7704076900505988 Prediction_local [-0.0426393] Right: 0.06394053846350395 Intercept 0.5119209992707148 Prediction_local [0.27354462] Right: 0.25931353696833803 Intercept -0.17875786784821335 Prediction_local [0.87887342] Right: 0.9999994397618931 Intercept 0.49316770567454327 Prediction_local [0.33963841] Right: 0.23437045234355897 Intercept 0.49343611165033363 Prediction_local [0.27490786] Right: 0.3441259772473614 Intercept 0.03437750132405204 Prediction_local [0.75616868] Right: 0.28083025072605144 Intercept 0.545435568825264 Prediction_local [0.217182] Right: 0.17463991221536082 Intercept 0.4331232059461694 Prediction_local [0.38578229] Right: 0.23839627995125126 Intercept 0.7866742750944831 Prediction_local [-0.06911598] Right: 3.208362155250929e-07 Intercept 0.49748131314929994 Prediction_local [0.286808] Right: 0.7901342555473342 Intercept 0.5427742925956992 Prediction_local [0.15993522] Right: 0.2738391520364248 Intercept 0.5522160093644802 Prediction_local [0.17149274] Right: 0.15122499494840438 Intercept 0.4832949047759558 Prediction_local [0.31859596] Right: 0.2355623673271266 Intercept 0.4923390098267605 Prediction_local [0.28495301] Right: 0.7048970974588349 Intercept 0.4901558827091324 Prediction_local [0.29700155] Right: 0.5490827143925748 Intercept 0.010644851032939773 Prediction_local [0.79546063] Right: 0.9821972425116082 Intercept 0.5265381460369447 Prediction_local [0.27650061] Right: 0.4709484986166547 Intercept 0.5063064840863167 Prediction_local [0.2905594] Right: 0.22348011947803645 Intercept 0.4584213169319494 Prediction_local [0.28448096] Right: 0.18367519992764897 Intercept 0.013507030610219628 Prediction_local [0.85906497] Right: 0.999080841575664 Intercept 0.33393772967260743 Prediction_local [0.37586675] Right: 5.537039765706517e-05 Intercept 0.5118444081087962 Prediction_local [0.27095834] Right: 0.4582059194020542 Intercept -0.024943340118481594 Prediction_local [0.93223964] Right: 0.8749728078520326 Intercept 0.5009966483061634 Prediction_local [0.29490334] Right: 0.164717638049228 Intercept 0.5621609439256032 Prediction_local [0.17639346] Right: 0.056320391632467916 Intercept 0.0850705849522819 Prediction_local [0.55078654] Right: 0.9999262095750617 Intercept 0.501028425991043 Prediction_local [0.27492544] Right: 0.35586576017112076 Intercept 0.5060642430833471 Prediction_local [0.1988483] Right: 0.43258722797868154 Intercept 0.5143469145401729 Prediction_local [0.27551823] Right: 0.6720046102678615 Intercept 0.5258531634707815 Prediction_local [0.24696691] Right: 0.41173465548407695 Intercept 0.4860910370056192 Prediction_local [0.28160788] Right: 0.07434057788865323 Intercept 0.5011674107380786 Prediction_local [0.28004547] Right: 0.11897856297228623 Intercept 0.49771730181479545 Prediction_local [0.28599557] Right: 0.3158188694906174 Intercept 0.48511301445070365 Prediction_local [0.28594182] Right: 0.45711455435860443 Intercept 0.5398320925516905 Prediction_local [0.19482224] Right: 0.5864544154604229 Intercept 0.49595530233436136 Prediction_local [0.28290142] Right: 0.2847177597217093 Intercept 0.49760503278219026 Prediction_local [0.29831463] Right: 0.07882820133015624 Intercept 0.4728658060020159 Prediction_local [0.28901366] Right: 0.29759495497302263 Intercept 0.5298148104615346 Prediction_local [0.16596212] Right: 0.08758679265259868 Intercept 0.4740575408602765 Prediction_local [0.28025227] Right: 0.20516880252552708 Intercept 0.4986875781570917 Prediction_local [0.25612484] Right: 0.14770662971219878 Intercept 0.5281968543927285 Prediction_local [0.18126318] Right: 0.2750197091911813 Intercept 0.4941489040773972 Prediction_local [0.2854916] Right: 0.31224087620792246 Intercept 0.4737539123566492 Prediction_local [0.37372383] Right: 0.1461236303345805 Intercept 0.7532798505100818 Prediction_local [0.06136537] Right: 0.07799763346979746 Intercept 0.008561485706532423 Prediction_local [0.78199209] Right: 0.999706307419314 Intercept 0.7406902397768469 Prediction_local [0.09500884] Right: 1.4302339137181426e-07 Intercept 0.474513447038701 Prediction_local [0.28614486] Right: 0.36330042821358943 Intercept 0.4652854290628131 Prediction_local [0.40753456] Right: 0.3852831527415579 Intercept 0.7608822318197246 Prediction_local [0.01896371] Right: 0.060216697863244545 Intercept 0.49804379368023843 Prediction_local [0.27992708] Right: 0.3919029144037455 Intercept 0.489158733691891 Prediction_local [0.26982766] Right: 0.07805808537002965 Intercept 0.7630105468876965 Prediction_local [0.03644969] Right: 4.305807628564569e-06 Intercept 0.4446234024488984 Prediction_local [0.35415566] Right: 0.3917352551530277 Intercept 0.48982059120207877 Prediction_local [0.30562459] Right: 0.1051650161729494 Intercept 0.4950716006095871 Prediction_local [0.35093798] Right: 0.15761617025791688 Intercept 0.6791959258322271 Prediction_local [0.23404306] Right: 0.0005834711988731187 Intercept 0.5419021765225993 Prediction_local [0.19121837] Right: 0.5849735125038231 Intercept 0.49861915266961876 Prediction_local [0.20989278] Right: 0.41036295680059676 Intercept 0.5469169907197347 Prediction_local [0.15624246] Right: 0.1437445328132137 Intercept 0.5072773463936606 Prediction_local [0.28634563] Right: 0.358426233860643 Intercept 0.7437981843050437 Prediction_local [0.07142026] Right: 2.563307434190744e-05 Intercept 0.5037343718596157 Prediction_local [0.27966737] Right: 0.28168863248262127 Intercept 0.496251000204371 Prediction_local [0.31601478] Right: 0.6681963849371239 Intercept 0.48149263192032654 Prediction_local [0.3047734] Right: 0.2914323672335651 Intercept 0.53370232315616 Prediction_local [0.28978559] Right: 0.1569138193202226 Intercept -0.16236241391938938 Prediction_local [0.93595881] Right: 0.9999975220339193 Intercept 0.5186628611236451 Prediction_local [0.24401367] Right: 0.01925001908731053 Intercept 0.03174791763494761 Prediction_local [0.77744156] Right: 0.7485289782899177 Intercept 0.5004400525382793 Prediction_local [0.31502202] Right: 0.14313781331509084 Intercept 0.7749813877377507 Prediction_local [-0.08588119] Right: 6.381697496090401e-07 Intercept 0.4758067617576414 Prediction_local [0.32019608] Right: 0.3121898665142583 Intercept 0.4060827528312245 Prediction_local [0.49071676] Right: 0.5029836773778776 Intercept 0.5430471413610283 Prediction_local [0.19635875] Right: 0.34327349277954206 Intercept 0.5500300766695712 Prediction_local [0.20771194] Right: 0.3553016047909115 Intercept 0.47741101860292684 Prediction_local [0.2851049] Right: 0.8819559446491994 Intercept 0.5013050564473361 Prediction_local [0.30920008] Right: 0.3009087804123412 Intercept 0.5224078467473574 Prediction_local [0.19704317] Right: 0.17961385756699558 Intercept 0.4955311718931336 Prediction_local [0.29303313] Right: 0.20633430540865255 Intercept 0.4866066946715587 Prediction_local [0.29655407] Right: 0.5560670633785338 Intercept 0.38408162762238884 Prediction_local [0.50244468] Right: 0.045679209115114305 Intercept 0.5582179798287561 Prediction_local [0.15607716] Right: 0.20956644497967367 Intercept 0.5638280786259047 Prediction_local [0.19460261] Right: 0.03368073247679197 Intercept 0.7369497167764419 Prediction_local [0.027216] Right: 0.006635121074970262 Intercept 0.7616917985602393 Prediction_local [0.01709416] Right: 1.173735638539799e-06 Intercept 0.48961917184467985 Prediction_local [0.31403332] Right: 0.392304901209755 Intercept 0.7339936047386317 Prediction_local [0.02593144] Right: 0.32244534488507315 Intercept 0.4681889606505277 Prediction_local [0.29753648] Right: 0.604971457901315 Intercept 0.7679957985404009 Prediction_local [0.06489676] Right: 0.955174642417321 Intercept 0.49790425416680517 Prediction_local [0.24659427] Right: 0.04204014635732016 Intercept 0.47289869906555204 Prediction_local [0.29694965] Right: 0.2068770300592745 Intercept -0.22623673925021698 Prediction_local [1.00876505] Right: 0.9999999892163319 Intercept 0.478541738000116 Prediction_local [0.29606179] Right: 0.4587516341188878 Intercept 0.48449031772719064 Prediction_local [0.28592541] Right: 0.26051427768624863 Intercept 0.70050698048229 Prediction_local [0.22647238] Right: 2.246919443990624e-06 Intercept 0.496450760477058 Prediction_local [0.30955162] Right: 0.2065167660067746 Intercept 0.552582379555044 Prediction_local [0.110656] Right: 0.11652714458056951 Intercept 0.49067645859167996 Prediction_local [0.28065345] Right: 0.06952376146433747 Intercept 0.031980470897187974 Prediction_local [0.75304607] Right: 0.9993391111358118 Intercept 0.506169737166141 Prediction_local [0.27602846] Right: 0.5489099093671075 Intercept 0.5079279535284245 Prediction_local [0.26759676] Right: 0.483445654501766 Intercept 0.038138043396702265 Prediction_local [0.74191111] Right: 0.999317448095705 Intercept 0.5553280389069024 Prediction_local [0.19230137] Right: 0.5937876414951028 Intercept 0.7194772233491731 Prediction_local [0.06144933] Right: 0.008044445431832435 Intercept 0.8380516485515074 Prediction_local [-0.0855455] Right: 1.7847422848642562e-05 Intercept 0.4910126952327332 Prediction_local [0.273425] Right: 0.22604595804129335 Intercept 0.5310550653893406 Prediction_local [0.28237981] Right: 0.3533122668880336 Intercept 0.4728368209830565 Prediction_local [0.31719013] Right: 0.13631954213516323 Intercept 0.5330546023458493 Prediction_local [0.12029376] Right: 0.1339005034351151 Intercept 0.465603895816469 Prediction_local [0.31274853] Right: 0.36492906272751596 Intercept 0.5026791905940922 Prediction_local [0.28990913] Right: 0.28349145129321107 Intercept 0.5145219420937944 Prediction_local [0.18071727] Right: 0.20584753439011458 Intercept 0.4569495000484766 Prediction_local [0.27793664] Right: 0.19570523807622223 Intercept 0.46528148088613996 Prediction_local [0.27644556] Right: 0.041081413905792244 Intercept 0.5019090875150155 Prediction_local [0.25618325] Right: 0.5233352618854975 Intercept 0.5032314748863806 Prediction_local [0.30123157] Right: 0.21771922693105106 Intercept 0.5448850019788711 Prediction_local [0.17444822] Right: 0.1046139958365679 Intercept 0.5216890114493686 Prediction_local [0.27306841] Right: 0.4737520869042051 Intercept 0.48976206299885106 Prediction_local [0.30605903] Right: 0.20388558085539324 Intercept 0.7655410989096014 Prediction_local [0.0271114] Right: 0.11703450072762676 Intercept 0.49965632037097657 Prediction_local [0.27902045] Right: 0.3408095817758115 Intercept 0.7966912998431861 Prediction_local [-0.07956515] Right: 1.0227964629000484e-06 Intercept 0.4298050244741603 Prediction_local [0.36583551] Right: 0.17631019173651769 Intercept 0.44933319913066994 Prediction_local [0.36262197] Right: 0.10615969830831828 Intercept 0.7672699507758381 Prediction_local [0.04250313] Right: 0.048963392703018055 Intercept 0.5457862167593006 Prediction_local [0.14228127] Right: 0.4470566257280398 Intercept 0.4501983862081772 Prediction_local [0.41922271] Right: 0.12142671594668138 Intercept 0.5443115529847775 Prediction_local [0.21484865] Right: 0.4845777781567068 Intercept 0.7326995919325723 Prediction_local [0.02087923] Right: 0.028380409283930753 Intercept 0.515595434776562 Prediction_local [0.20101694] Right: 0.12662164777757853 Intercept 0.5203438091190717 Prediction_local [0.2823669] Right: 0.29283001874524645 Intercept -0.22862484060436583 Prediction_local [0.99538527] Right: 0.9999674985032418 Intercept 0.512232236081576 Prediction_local [0.20561718] Right: 0.5577738100359207 Intercept 0.49721810545774847 Prediction_local [0.3206873] Right: 0.08587252417958893 Intercept 0.49823061086460024 Prediction_local [0.26524141] Right: 0.11479771612189864 Intercept 0.5246077713957618 Prediction_local [0.25210967] Right: 0.1003848057342172 Intercept 0.4924132677583104 Prediction_local [0.2967821] Right: 0.34867753047720546 Intercept 0.024085753316257785 Prediction_local [0.74137056] Right: 0.7481917041550971 Intercept 0.5043656917184839 Prediction_local [0.28881573] Right: 0.33782051507986166 Intercept 0.514965560863313 Prediction_local [0.24250376] Right: 0.19139830012328418 Intercept 0.5590127881367357 Prediction_local [0.153689] Right: 0.19431967927113603 Intercept 0.4695239677817423 Prediction_local [0.28927119] Right: 0.4385244823321052 Intercept 0.7664977760852186 Prediction_local [0.06527895] Right: 0.6485746030376252 Intercept 0.5726615881652777 Prediction_local [0.11655402] Right: 0.005226151536579883 Intercept 0.6890418165896413 Prediction_local [0.24029876] Right: 2.249879841292958e-06 Intercept 0.5824813039557607 Prediction_local [0.11851919] Right: 0.09667425677286436 Intercept 0.5159842150136813 Prediction_local [0.26615832] Right: 0.15219770055413023 Intercept 0.4835159122160569 Prediction_local [0.30736554] Right: 0.5043055790758605 Intercept 0.4935615705226969 Prediction_local [0.29030125] Right: 0.26758389279560835 Intercept 0.4653733308692516 Prediction_local [0.29657513] Right: 0.12726680940366186 Intercept 0.5208392763884322 Prediction_local [0.26704257] Right: 0.27602303441161175 Intercept 0.5132314032212263 Prediction_local [0.2041117] Right: 0.5790945297482462 Intercept 0.7945984271602697 Prediction_local [-0.12136135] Right: 0.0001171808643834374 Intercept 0.48882768562008877 Prediction_local [0.32227316] Right: 0.4729826010640354 Intercept 0.40393238068859777 Prediction_local [0.48022098] Right: 0.15078021058330715 Intercept 0.48797117758818415 Prediction_local [0.30188067] Right: 0.07951962615999168 Intercept 0.49465358688968963 Prediction_local [0.29602912] Right: 0.17842194726764116 Intercept 0.034213353945994784 Prediction_local [0.87075419] Right: 0.9637475660646669 Intercept 0.5439252700354302 Prediction_local [0.18789203] Right: 0.24397817591859208 Intercept 0.5049436688198373 Prediction_local [0.30992257] Right: 0.22091869758706661 Intercept 0.4806788584219914 Prediction_local [0.32045946] Right: 0.21358470545979685 Intercept 0.5260330086840156 Prediction_local [0.27493703] Right: 0.3708396448478339 Intercept 0.7994259587744832 Prediction_local [-0.08998936] Right: 0.2753289887940681 Intercept 0.512102769806419 Prediction_local [0.30713403] Right: 0.43360432449235775 Intercept -0.02570396011683629 Prediction_local [0.90318169] Right: 0.999943012086774 Intercept 0.014173026289585 Prediction_local [0.74659863] Right: 0.9885210273596863 Intercept 0.4757885947786365 Prediction_local [0.31242488] Right: 0.2781305924739409 Intercept 0.4555553665554465 Prediction_local [0.37356016] Right: 0.16018365245064203 Intercept 0.49039708221002676 Prediction_local [0.26612123] Right: 0.10380668404683417 Intercept 0.4747324027463914 Prediction_local [0.32557434] Right: 0.2639458900947653 Intercept 0.2679381925332191 Prediction_local [0.52016704] Right: 0.02122754119700713 Intercept 0.5419091093206563 Prediction_local [0.17302595] Right: 0.14816933688300613 Intercept 0.4745859575812411 Prediction_local [0.29864928] Right: 0.28020402982895987 Intercept 0.5297226123919854 Prediction_local [0.22487374] Right: 0.306992791142008 Intercept 0.4894053636515222 Prediction_local [0.26917897] Right: 0.4124075417514665 Intercept 0.44699514441112737 Prediction_local [0.39211175] Right: 0.10046416534235228 Intercept 0.510896778702457 Prediction_local [0.17451738] Right: 0.36393468899532694 Intercept 0.5507562590585395 Prediction_local [0.21212417] Right: 0.07267976800120172 Intercept 0.41935608530396284 Prediction_local [0.4864902] Right: 0.16735182088442088 Intercept 0.7647762592280327 Prediction_local [0.08547694] Right: 1.677968768825116e-05 Intercept 0.4665644952138434 Prediction_local [0.28721536] Right: 0.26657946898554963 Intercept 0.4990382710173932 Prediction_local [0.27645109] Right: 0.12520040683393524 Intercept 0.7454807400668158 Prediction_local [0.00842558] Right: 0.3630141327397499 Intercept 0.7450104765469663 Prediction_local [0.04367548] Right: 0.13015817134154145 Intercept 0.5623649553798238 Prediction_local [0.15439202] Right: 0.2004291815961433 Intercept 0.5368542561369734 Prediction_local [0.14071315] Right: 0.13333820233825774 Intercept 0.4596356007198529 Prediction_local [0.31626157] Right: 0.38553723070783735 Intercept 0.45087441970091147 Prediction_local [0.37848739] Right: 0.36691587855587576 Intercept 0.7196135545725229 Prediction_local [0.0515827] Right: 2.230093729014363e-07 Intercept 0.55797334010255 Prediction_local [0.14017849] Right: 0.0029722583584120754 Intercept 0.48678787994993344 Prediction_local [0.27225609] Right: 0.4438893452405069 Intercept 0.5070783805638038 Prediction_local [0.28648352] Right: 0.043037115457054485 Intercept 0.4967302337384025 Prediction_local [0.30181167] Right: 0.6818817995499832 Intercept 0.47142252419605446 Prediction_local [0.31555628] Right: 0.8085395354281941 Intercept 0.5018539168732298 Prediction_local [0.3416621] Right: 0.2970698445483241 Intercept 0.4765679595443339 Prediction_local [0.40812959] Right: 0.21501112593412308 Intercept 0.47626279014654305 Prediction_local [0.38234608] Right: 0.36003642904523486 Intercept 0.7531982245507154 Prediction_local [0.03954835] Right: 1.6334807060470163e-06 Intercept 0.5077705967463079 Prediction_local [0.30533681] Right: 0.5174326092036359 Intercept 0.7360572722660867 Prediction_local [0.05557299] Right: 5.232421534902963e-06 Intercept 0.4293319864076758 Prediction_local [0.43047367] Right: 0.43432400330631926 Intercept 0.48000948446816805 Prediction_local [0.3206899] Right: 0.11923897478900236 Intercept 0.7950940175295242 Prediction_local [-0.07348402] Right: 2.6464405993718205e-07 Intercept 0.49934060806651503 Prediction_local [0.3042411] Right: 0.4005877578606603 Intercept 0.48808642060229235 Prediction_local [0.30142565] Right: 0.47582800496182714 Intercept 0.4784129310162357 Prediction_local [0.31165315] Right: 0.2942735187731146 Intercept 0.5171962306190913 Prediction_local [0.17608872] Right: 0.03821433418891603 Intercept 0.49669230581117735 Prediction_local [0.31385473] Right: 0.12189243640518777 Intercept 0.4159136020755127 Prediction_local [0.50597798] Right: 0.016132240971285454 Intercept 0.5432661799861437 Prediction_local [0.17558714] Right: 0.062139367929881596 Intercept 0.4951916340945437 Prediction_local [0.2956907] Right: 0.5549558734426601 Intercept 0.5245384176801495 Prediction_local [0.23194632] Right: 0.026587832865186026 Intercept 0.8337132487047272 Prediction_local [-0.1290075] Right: 0.02484863576386282 Intercept 0.49717176350032316 Prediction_local [0.25540494] Right: 0.27501146623276806 Intercept 0.563527181948601 Prediction_local [0.11124028] Right: 0.21459325852894498 Intercept 0.47308342688720073 Prediction_local [0.27911149] Right: 0.4626394896059243 Intercept 0.4737123074403129 Prediction_local [0.32186392] Right: 0.0033405007030594797 Intercept 0.4934316677909878 Prediction_local [0.26815694] Right: 0.6867469960542822 Intercept 0.4889456151198869 Prediction_local [0.32301947] Right: 0.22542403267279237 Intercept 0.43660925676359996 Prediction_local [0.41850537] Right: 0.5330549023861096 Intercept 0.5453499996175708 Prediction_local [0.18086706] Right: 0.5401159795452636 Intercept 0.5179802632321315 Prediction_local [0.27968823] Right: 0.41947351100044156 Intercept 0.49132641674762023 Prediction_local [0.29901361] Right: 0.20618731726373693 Intercept 0.48743867767512716 Prediction_local [0.32069486] Right: 0.1146912192718135 Intercept 0.4383279917674183 Prediction_local [0.39207089] Right: 0.018718602483930275 Intercept 0.5203609737988648 Prediction_local [0.25581173] Right: 0.2692806878282222 Intercept 0.5480880185097626 Prediction_local [0.1684961] Right: 0.14348156150373534 Intercept 0.4721674762497722 Prediction_local [0.39038519] Right: 0.47254036809086786 Intercept 0.49835936997936997 Prediction_local [0.26741612] Right: 0.09046750389618927 Intercept 0.49210164365860987 Prediction_local [0.29713586] Right: 0.1700495761582965 Intercept 0.45322429525827523 Prediction_local [0.39558362] Right: 0.22937434487589317 Intercept 0.4868462884227486 Prediction_local [0.25398371] Right: 0.3254917332993936 Intercept 0.4261886784668105 Prediction_local [0.46711025] Right: 0.23733384113164013 Intercept 0.5247088889836067 Prediction_local [0.23911135] Right: 0.12551228071562384 Intercept 0.6911018228983825 Prediction_local [0.20616983] Right: 5.871317690405873e-10 Intercept 0.5271185108922362 Prediction_local [0.2574962] Right: 0.22431635424474966 Intercept 0.49801963084100936 Prediction_local [0.29792716] Right: 0.4835596662895015 Intercept 0.5605423995264591 Prediction_local [0.21285402] Right: 0.1469207155200401 Intercept 0.5090900700106264 Prediction_local [0.15746496] Right: 0.0893557921280419 Intercept 0.49753160833307675 Prediction_local [0.27903797] Right: 0.16386432399294865 Intercept 0.4713416589481161 Prediction_local [0.3065339] Right: 0.09030079717685707 Intercept 0.5143449138729524 Prediction_local [0.27591387] Right: 0.051589809547193145 Intercept 0.49173006953973275 Prediction_local [0.28328995] Right: 0.6357369764961729 Intercept 0.5350393606621002 Prediction_local [0.17098846] Right: 0.163208062042027 Intercept 0.5372566688254505 Prediction_local [0.13799369] Right: 0.18049556856014198 Intercept 0.026084635232738462 Prediction_local [0.80768732] Right: 0.9998184099843234 Intercept 0.5064650696980054 Prediction_local [0.32826853] Right: 0.49907487175567977 Intercept 0.4842834054919135 Prediction_local [0.35055285] Right: 0.3462704646088412 Intercept 0.4844167440070808 Prediction_local [0.29434726] Right: 0.2561428997698415 Intercept 0.5170049768235876 Prediction_local [0.34266271] Right: 0.1151297623808747 Intercept 0.5583169331398365 Prediction_local [0.206478] Right: 0.13331875060488782 Intercept 0.5019306529948149 Prediction_local [0.2503468] Right: 0.1003848057342172 Intercept 0.5021617570994237 Prediction_local [0.29601305] Right: 0.21360303190150357 Intercept 0.5128817792641057 Prediction_local [0.30082946] Right: 0.18139643909089398 Intercept 0.5257498542631003 Prediction_local [0.20230561] Right: 0.24868524933426725 Intercept 0.7564020342703561 Prediction_local [0.04039892] Right: 0.8345416181824894 Intercept 0.5371586492746439 Prediction_local [0.16225008] Right: 0.36237840318080256 Intercept 0.5114777361741477 Prediction_local [0.27062289] Right: 0.02310340757709468 Intercept 0.7843409250922642 Prediction_local [0.05321456] Right: 0.40540235724885637 Intercept 0.7228507997971272 Prediction_local [0.04783134] Right: 1.8706839723652917e-06 Intercept 0.5465617877665687 Prediction_local [0.20986083] Right: 0.46215537933824713 Intercept 0.4963579448998403 Prediction_local [0.29470253] Right: 0.37496458823039563 Intercept 0.7833984088813315 Prediction_local [-0.03335153] Right: 1.6403326934090088e-06 Intercept 0.013507979621679989 Prediction_local [0.76379977] Right: 0.9999940459126013 Intercept 0.27870737542352925 Prediction_local [0.48951453] Right: 0.0023420916451490667 Intercept 0.4477086072308597 Prediction_local [0.37914706] Right: 0.3955562090685172 Intercept 0.8112050466040451 Prediction_local [-0.03710764] Right: 0.00020995062420196129 Intercept 0.7633419244305824 Prediction_local [0.05524145] Right: 0.5617929223923348 Intercept 0.49221258864143075 Prediction_local [0.26881459] Right: 0.2441168138553621 Intercept 0.492004951953525 Prediction_local [0.28203392] Right: 0.05560979251449409 Intercept 0.5191324413852568 Prediction_local [0.28549224] Right: 0.26487907582274867 Intercept 0.4823273887354902 Prediction_local [0.28930293] Right: 0.13761834796268335 Intercept 0.4736593056079498 Prediction_local [0.30116455] Right: 0.23519502079054383 Intercept 0.5026867634654472 Prediction_local [0.26585196] Right: 0.24217009425858402 Intercept 0.6783788989328589 Prediction_local [0.22492604] Right: 0.23700874515424092 Intercept 0.4053918566686898 Prediction_local [0.50018218] Right: 0.04960055680884135 Intercept 0.5006195761091442 Prediction_local [0.28615332] Right: 0.21565689636321497 Intercept 0.4079966238988767 Prediction_local [0.48743273] Right: 0.3831219383223579 Intercept 0.49400676492483153 Prediction_local [0.28506666] Right: 0.09520378635175232 Intercept 0.5798577922862402 Prediction_local [0.15629328] Right: 0.4733070117379418 Intercept 0.026513396399907407 Prediction_local [0.77340479] Right: 0.9827773063763119 Intercept 0.7532908393165864 Prediction_local [0.05911624] Right: 0.9237904932951908 Intercept 0.500303244564469 Prediction_local [0.2717639] Right: 0.3122366802767785 Intercept 0.39055095722331645 Prediction_local [0.50259315] Right: 0.20077978116790507 Intercept 0.6813726316123485 Prediction_local [0.19918983] Right: 8.976347823603069e-07 Intercept 0.5460126226136771 Prediction_local [0.18405371] Right: 0.47410294042315987 Intercept 0.7642224740893426 Prediction_local [0.03613975] Right: 5.846945012585074e-07 Intercept 0.7252639561046321 Prediction_local [0.15316107] Right: 0.005963493431154653 Intercept 0.033925455528966086 Prediction_local [0.77981765] Right: 0.9748170169596674 Intercept 0.7381923689514314 Prediction_local [0.05090122] Right: 0.11811111560981981 Intercept 0.4358267334435189 Prediction_local [0.43251964] Right: 0.42655573782134815 Intercept 0.5682151616863923 Prediction_local [0.20147317] Right: 0.351821068133829 Intercept 0.46439473575775375 Prediction_local [0.35445613] Right: 0.24522546385803645 Intercept 0.5121301335281943 Prediction_local [0.21160253] Right: 0.05655793288005606 Intercept 0.025290511317631437 Prediction_local [0.75755763] Right: 0.9825797421221961 Intercept 0.5523050947986229 Prediction_local [0.21455969] Right: 0.3637519748924829 Intercept 0.5376784971749355 Prediction_local [0.22477093] Right: 0.2021656810082254 Intercept 0.7402374720425329 Prediction_local [0.05921099] Right: 0.00031941515634096717 Intercept -0.3124055495308846 Prediction_local [1.1725174] Right: 0.9999999062840331 Intercept 0.4685071443453621 Prediction_local [0.38244517] Right: 0.15019712333886923 Intercept 0.47145791275226545 Prediction_local [0.29799606] Right: 0.11650555570113401 Intercept 0.4283468403545855 Prediction_local [0.44927654] Right: 0.05412627702972953 Intercept 0.5428082374991126 Prediction_local [0.15950659] Right: 0.3936375238826852 Intercept 0.5005188460095424 Prediction_local [0.28123526] Right: 0.5220372586859657 Intercept 0.7196671380293718 Prediction_local [0.06173772] Right: 7.589061820030484e-07 Intercept 0.026241578665613385 Prediction_local [0.76090257] Right: 0.9997214334498459 Intercept 0.7658049716359339 Prediction_local [0.03449497] Right: 3.6832042697627923e-07 Intercept 0.4760983546414913 Prediction_local [0.27655271] Right: 0.14865531276368563 Intercept 0.49675068255388255 Prediction_local [0.29310952] Right: 0.2629111951886411 Intercept 0.4820863050239684 Prediction_local [0.30048911] Right: 0.31342602346134135 Intercept 0.4007755441394904 Prediction_local [0.51634541] Right: 0.3204835910957543 Intercept 0.50495538517939 Prediction_local [0.19073988] Right: 0.5621803274064052 Intercept 0.4876903730663239 Prediction_local [0.30439095] Right: 0.12363473082515808 Intercept -0.18793537406419858 Prediction_local [0.89765543] Right: 0.999999986280492 Intercept 0.4752357096686466 Prediction_local [0.29984048] Right: 0.07001345519888666 Intercept 0.7083146141446122 Prediction_local [0.13214317] Right: 0.6143628970535714 Intercept 0.4063376696178578 Prediction_local [0.48985017] Right: 0.046255469824131534 Intercept 0.5083398151580832 Prediction_local [0.28828081] Right: 0.40328479786739807 Intercept 0.4927451697782472 Prediction_local [0.28509917] Right: 0.12495065078178408 Intercept 0.5373903254039554 Prediction_local [0.22374052] Right: 0.2413563099268522 Intercept 0.44569195034426456 Prediction_local [0.39401587] Right: 0.4266166989880802 Intercept 0.4228515410707534 Prediction_local [0.39823224] Right: 0.1864882130439075 Intercept 0.5349295170458404 Prediction_local [0.19468456] Right: 0.30196662530282814 Intercept 0.7392745991363814 Prediction_local [0.01428213] Right: 1.8690014144882303e-06 Intercept 0.7317186038884644 Prediction_local [0.05646888] Right: 0.21412826898757484 Intercept 0.4302858639295948 Prediction_local [0.39091807] Right: 0.023667824885161903 Intercept 0.4483797438984555 Prediction_local [0.39344839] Right: 0.14627693272906003 Intercept 0.4355818246919611 Prediction_local [0.48865746] Right: 0.43056084123166916 Intercept 0.5470311330634032 Prediction_local [0.17812801] Right: 0.1963471076709516 Intercept 0.4949854077865998 Prediction_local [0.29856778] Right: 0.40435480410166014 Intercept 0.41310964123310656 Prediction_local [0.45535956] Right: 0.014181576215086956 Intercept 0.2765918375522357 Prediction_local [0.58268979] Right: 0.2978036006157111 Intercept 0.46816328711221344 Prediction_local [0.32492758] Right: 0.14167242502194305 Intercept 0.5044049581748558 Prediction_local [0.19746973] Right: 0.4819023513152863 Intercept 0.7091850584637276 Prediction_local [0.1362213] Right: 0.20639570581069555 Intercept 0.5020454916363235 Prediction_local [0.31138053] Right: 0.39525872081576247 Intercept 0.5179589059467595 Prediction_local [0.29609385] Right: 0.4577183492996648 Intercept 0.5075665318053548 Prediction_local [0.28579657] Right: 0.1358028257100947 Intercept 0.4801170369073401 Prediction_local [0.30501457] Right: 0.0485296777039603 Intercept 0.8206569949824325 Prediction_local [-0.13928715] Right: 0.03145128356636698 Intercept 0.5635225156575892 Prediction_local [0.21427366] Right: 0.16048244139561735 Intercept 0.4816726224186725 Prediction_local [0.34791522] Right: 0.3590836648997414 Intercept 0.7522635131824691 Prediction_local [0.02829262] Right: 5.720623554123094e-06 Intercept 0.5157711875265932 Prediction_local [0.18650034] Right: 0.16818853912789417 Intercept 0.4769595215486093 Prediction_local [0.29343811] Right: 0.2683286248090269 Intercept 0.7722111651152497 Prediction_local [0.04129039] Right: 3.34247778490898e-06 Intercept 0.470091312559347 Prediction_local [0.34242494] Right: 0.5292816284795957 Intercept 0.4835354866267204 Prediction_local [0.28050885] Right: 0.14703208682704194 Intercept 0.03580473186913771 Prediction_local [0.75536873] Right: 0.9928301750412062 Intercept 0.7505547795642374 Prediction_local [0.07435492] Right: 0.00010915659238681898 Intercept 0.48071217335023875 Prediction_local [0.30552031] Right: 0.044043227808070316 Intercept 0.44651133059007864 Prediction_local [0.32353701] Right: 0.34582900424191215 Intercept 0.03473857323357504 Prediction_local [0.76238372] Right: 0.9982597855153722 Intercept 0.48271632631244993 Prediction_local [0.32693686] Right: 0.8524747081559183 Intercept 0.4755547374542565 Prediction_local [0.30923281] Right: 0.3014781868048903 Intercept 0.029581435352898366 Prediction_local [0.75712342] Right: 0.9992138512527332 Intercept 0.454412971079562 Prediction_local [0.30798203] Right: 0.21807961968725803 Intercept 0.4164729820407305 Prediction_local [0.45369945] Right: 0.0808573517978911 Intercept 0.4819652697902699 Prediction_local [0.28411091] Right: 0.39790110534780343 Intercept 0.5009944502056404 Prediction_local [0.29537369] Right: 0.846806653456075 Intercept 0.5072195872536781 Prediction_local [0.26863879] Right: 0.24375874521892704 Intercept 0.4910166442432079 Prediction_local [0.29292138] Right: 0.21104548481839033 Intercept 0.47097832453532773 Prediction_local [0.29959965] Right: 0.5371326506590502 Intercept 0.4654742556426681 Prediction_local [0.28775717] Right: 0.3424923846676156 Intercept 0.7193411343781297 Prediction_local [0.10220941] Right: 0.23565307475755448 Intercept 0.49984383352741957 Prediction_local [0.28314263] Right: 0.0412463584181111 Intercept 0.5279873632101717 Prediction_local [0.21296903] Right: 0.019075315972603583 Intercept 0.7469034870285467 Prediction_local [-0.01170049] Right: 0.2835379021266758 Intercept 0.7286926538053562 Prediction_local [0.05671181] Right: 0.0003337856069630019 Intercept 0.49388818772667853 Prediction_local [0.30248233] Right: 0.19563082688871836 Intercept 0.4910109413934589 Prediction_local [0.30986561] Right: 0.15133974185695684 Intercept 0.46483450229267764 Prediction_local [0.37762477] Right: 0.46626284663931533 Intercept -0.20272978233894223 Prediction_local [1.01470598] Right: 0.9999999999775366 Intercept 0.019677942209979093 Prediction_local [0.80289854] Right: 0.9994993207688792 Intercept 0.04046474364205366 Prediction_local [0.75767361] Right: 0.9926127310595523 Intercept 0.524379920079904 Prediction_local [0.26591553] Right: 0.09039280021317703 Intercept 0.463273643083163 Prediction_local [0.2770955] Right: 0.19352046607642004 Intercept 0.748854357278036 Prediction_local [0.04317769] Right: 0.6754804808560331 Intercept 0.7458220127801014 Prediction_local [0.05954827] Right: 3.173179279406124e-07 Intercept 0.03792834888930474 Prediction_local [0.73555925] Right: 0.8606576051804332 Intercept 0.48508613208643114 Prediction_local [0.29122502] Right: 0.6105986798105792 Intercept 0.7469416000883982 Prediction_local [0.0006561] Right: 0.020921983962884208 Intercept 0.42898134446588676 Prediction_local [0.4698003] Right: 0.4522843896391205 Intercept 0.48959903986994474 Prediction_local [0.29291366] Right: 0.4194048982110552 Intercept 0.5061776446401616 Prediction_local [0.27425945] Right: 0.1073711632379113 Intercept 0.5162301486975667 Prediction_local [0.27041715] Right: 0.1924669313559401 Intercept 0.4759944630858878 Prediction_local [0.27130853] Right: 0.12132314435239613 Intercept 0.4958874860799923 Prediction_local [0.28389574] Right: 0.3194540205279071 Intercept 0.5210233900673328 Prediction_local [0.27220607] Right: 0.4058886466407405
In [ ]:
# Display the selected explanations
for i, exp in enumerate(sp_explanations):
print(f"\nSubmodular Pick Explanation {i+1} (Sample index: {sp_indices[i]})")
exp.show_in_notebook(show_table=True)
Submodular Pick Explanation 1 (Sample index: 3386)
Submodular Pick Explanation 2 (Sample index: 3891)
Submodular Pick Explanation 3 (Sample index: 1260)
Submodular Pick Explanation 4 (Sample index: 1608)
Submodular Pick Explanation 5 (Sample index: 1942)
Submodular Pick Explanation 6 (Sample index: 3432)
Submodular Pick Explanation 7 (Sample index: 3607)
Submodular Pick Explanation 8 (Sample index: 3553)
Submodular Pick Explanation 9 (Sample index: 3758)
Submodular Pick Explanation 10 (Sample index: 865)